Big data surveillance across fields: Algorithmic governance for policing & regulation

Abstract

While the academic separation of policing and regulation is still largely operative, points of convergence are more significant than ever in the digital age, starting with concomitant debates about algorithms as a new figure of power. From the policing of illegal activities to the regulation of legal ones, the algorithmization of such critical social ordering practices has been the subject of growing attention. These burgeoning discussions are focused on one common element: big data surveillance. In accordance with such similarities and paralleled developments in policing and regulation, the article aims to further bridge the gap between literatures to respond to the calls for studying big data surveillance across institutional domains and social fields. To do so, it is focused on one case study that articulates algorithmic policing and regulation domains, in-between security and economic fields. This is the fight against illicit finance, i.e. ‘the global action against the financial flows that fuel crime and terrorism’. To what extent does big data surveillance make a difference in the main global policy of crime-fighting and financial regulation? Drawing on a fieldwork in a large North American bank, the present article takes stock of the algorithmic overlap between policing and regulation. It argues that the final result is policing and regulation of neither too much nor too little, which gives rise to automated and everyday mass surveillance while remaining as far removed from regulatory and crime-fighting ambitions as it is from dystopian visions of big data and algorithmic drama.

Disassembling the actant: A valediction to actor-network theory

“…Many social scientists interested in big data and AI in the last decade have been heavily influenced by Latour's work. Latour's lexicon of associations, assemblages, devices and infrastuctures suffuses discussions in this field (Amicelle, 2022;Aradau and Blanke, 2015;Valdivia et al, 2022). The implication is that AIs and their algorithms are actants which configure emergent tech networks.…”

“…Yet, creating and sustaining these algorithms requires human labour. As one of Amicelle's informants noted: 'It's a lot of work, it took a lot of time because you want to make sure that at the end of the day everything has been mapped' (Amicelle, 2022). Amicelle observes: 'This quote sheds light on the invisible and hard work that makes big data surveillance possible' (Amicelle, 2022).…”

Deploying artificial intelligence for anti-money laundering and asset recovery: the dawn of a new era

“…Faced with this heavy compliance burden, as well as increasingly aggressive AML enforcement in several jurisdictions, financial institutions must allocate more resources to support their compliance teams. To avoid hefty fines for non-compliance, they must be able to review a vast number of alerts generated by AML/CFT monitoring systems effectively and promptly (Amicelle, 2022;Partington, 2017). Consequently, financial institutions may need to spend up to 4% of their revenue on regulatory compliance (Walshe and Cropper, 2018;Duff and Phelps, 2017).…”